I asked the audience to imagine that I was running a game show. I announced that I would go along every row, starting at the front, and give each member a chance to say “cooperate” or “defect.” Each time someone said “defect” I would award a euro only to her. Each time someone said “cooperate” I would award ten cents to her and to everyone else in the audience. And I asked that they play this game solely to maximize their individual total score, without worrying about friendship, politeness, the common good, etc. I said that I would stop at an unpredictable point after at least twenty players had played

Like successive motivational states within a person, each successive player had a direct interest in the behavior of each subsequent player; and had to guess her future choices somewhat by noticing the choices already made. If she realized that her move would be the most salient of these choices right after she made it, she had an incentive to forego a sure euro, but only if she thought that this choice would be both necessary and sufficient to make later players do likewise.

In this kind of game, knowing the other players’ thoughts and characters– whether they are greedy, or devious, for instance—will not help you choose, as long as you believe them to be playing to maximize their monetary gains. This is so because the main determinant of their choices will be the pattern of previous members’ play at the moment of these choices. Retaliation for a defection will not occur punitively– a current player has no reason to reward or punish a player who will not play again — but what amounts to retaliation will happen through the effect of this defection on subsequent players’ estimations of their prospects and their consequent choices. These would seem to be the same considerations that bear on successive motivational states within a person, except that in this interpersonal game the reward for future cooperations is flat (ten cents per cooperation, discounted negligibly), rather than discounted in a hyperbolic curve depending on each reward’s delay.

Perceiving each choice as a test case for the climate of cooperation turns the activity into a positive feedback system—cooperations make further cooperations more likely, and defections make defections more likely. The continuous curve of motivation is broken into dichotomies, resolutions that either succeed or fail.

The phrase ‘tragedy of the commons’ was first popularised in an article about population control.

The rebuttal to the invisible hand in population control is to be found in a scenario first sketched in a little-known pamphlet in 1833 by a mathematical amateur named William Forster Lloyd (1794–1852). We may well call it “the tragedy of the commons,” using the word “tragedy” as the philosopher Whitehead used it: “The essence of dramatic tragedy is not unhappiness. It resides in the solemnity of the remorseless working of things.” He then goes on to say, “This inevitableness of destiny can only be illustrated in terms of human life by incidents which in fact involve unhappiness. For it is only by them that the futility of escape can be made evident in the drama.”

Ruin is the destination toward which all men rush, each pursuing his own best interest in a society that believes in the freedom of the commons.

…

When men mutually agreed to pass laws against robbing, mankind became more free, not less so. Individuals locked into the logic of the commons are free only to bring on universal ruin once they see the necessity of mutual coercion, they become free to pursue other goals.

…

The most important aspect of necessity that we must now recognize, is the necessity of abandoning the commons in breeding. No technical solution can rescue us from he misery of overpopulation. Freedom to breed will bring ruin to all.

Simplified models of artificial situations can be offered for either of two purposes. One is ambitious: these are “basic models” – first approximations that can be elaborated to simulate with higher fidelity the real situations we want to examine. The second is modest: whether or not these models constitute a “starting set” on which better approximations can be built, they illustrate the kind of analysis that is needed, some of the phenomena to be anticipated, and some of the questions worth asking.

The second, more modest, accomplishment is my only aim in the preceding demonstrations. The models were selected for easy description, easy visualization, and easy algebraic treatment. But even these artificial models invite elaboration. In the closed model [of self-sorting of a fixed population across two sub-groups (‘rooms’) according to individual’s preferences for a group mean age closest to their own], for example, we could invoke a new variable, perhaps “density”, and get a new division between the two rooms at a point where the greater attractiveness of the age level is balanced by the greater crowding. To do this requires interpreting “room” concretely rather than abstractly, with some physical dimension of some facility in short supply. (A child may prefer to be on the baseball squad which has older children, but not if he gets to play less frequently; a person may prefer to travel with an older group, but not if it reduces his chances of a window seat; a person may prefer the older discussion group, but not if it means a more crowded room, more noise, fewer turns at talking, and less chance of being elected chairman.) As we add dimensions to the model, and the model becomes more particular, we can be less confident that our model is of something we shall ever want to examine. And after a certain amount of heuristic experiments with building blocks, it becomes more productive to identify the actual characteristics of the phenomena we want to study, rather than to explore general properties of self-sorting on a continuous variable. Nursing homes, tennis clubs, bridge tournaments, social groupings, law firms, apartment buildings, undergraduate colleges, and dancing classes may display a number of similar phenomena in their membership; and there be a number of respects in which age, I.Q., walking speed, driving speed, income, seniority, body size, and social distinction motivate similar behaviours. But the success of analysis eventually depends as much on identifying what is peculiar to one of them as on the insight developed by studying what is common to them.

If you are on a bike and you get hit by a car, we say you were ‘tragically killed in a cycling accident’. However, if you are walking along and you get hit by a car, we do not say that you were tragically killed in a walking accident. Framing, eh?

We can’t define anything precisely. If we attempt to, we get into that paralysis of thought that comes to philosophers… one saying to the other: “you don’t know what you are talking about!”. The second one says: “what do you mean by talking? What do you mean by you? What do you mean by know?”